Triple

T9083187
Position Surface form Disambiguated ID Type / Status
Subject Han Yu E217683 entity
Predicate courtesyName P570 FINISHED
Object Tuizhi
Tuizhi is the courtesy name of Han Yu, a prominent Tang dynasty Confucian scholar, essayist, and poet known for advocating classical prose.
 E775716 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tuizhi | Statement: [Han Yu, courtesyName, Tuizhi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tuizhi
Context triple: [Han Yu, courtesyName, Tuizhi]
  • A. Pizhou
    Pizhou is a county-level city administered by Xuzhou in Jiangsu Province, eastern China, known for its historical sites and regional commerce.
  • B. Gaotangling
    Gaotangling is the town that serves as the administrative seat and political center of Wangcheng County in Hunan Province, China.
  • C. Hehuanshan
    Hehuanshan is a high-altitude mountain and popular scenic area in Taiwan, known for its alpine landscapes, hiking trails, and seasonal snow.
  • D. Zijincheng
    Zijincheng is the Chinese name for the Forbidden City, the vast imperial palace complex in central Beijing that served as the home of emperors and the political heart of China for nearly five centuries.
  • E. Xishan
    Xishan is the given name of Yan Xishan, a prominent Chinese warlord and political leader active in Shanxi during the early 20th century.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tuizhi
Triple: [Han Yu, courtesyName, Tuizhi]
Generated description
Tuizhi is the courtesy name of Han Yu, a prominent Tang dynasty Confucian scholar, essayist, and poet known for advocating classical prose.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tuizhi
Target entity description: Tuizhi is the courtesy name of Han Yu, a prominent Tang dynasty Confucian scholar, essayist, and poet known for advocating classical prose.
  • A. Pizhou
    Pizhou is a county-level city administered by Xuzhou in Jiangsu Province, eastern China, known for its historical sites and regional commerce.
  • B. Gaotangling
    Gaotangling is the town that serves as the administrative seat and political center of Wangcheng County in Hunan Province, China.
  • C. Hehuanshan
    Hehuanshan is a high-altitude mountain and popular scenic area in Taiwan, known for its alpine landscapes, hiking trails, and seasonal snow.
  • D. Zijincheng
    Zijincheng is the Chinese name for the Forbidden City, the vast imperial palace complex in central Beijing that served as the home of emperors and the political heart of China for nearly five centuries.
  • E. Xishan
    Xishan is the given name of Yan Xishan, a prominent Chinese warlord and political leader active in Shanxi during the early 20th century.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca83d7a0388190ba1af89ed7ba36f9 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc960a2760819084aab611eb1c43a9 completed April 1, 2026, 3:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69cffe30093481908b3ec394cf585642 completed April 3, 2026, 5:51 p.m.
NEDg Description generation batch_69d000d5b24481908679dbb92372c4b9 completed April 3, 2026, 6:03 p.m.
NED2 Entity disambiguation (via description) batch_69d001692c3481909924615717e316c2 completed April 3, 2026, 6:05 p.m.
Created at: March 30, 2026, 7:13 p.m.